In this paper a connectionist framework is outlined which combines the advantages symbolic and pajallel distributed processing. With regard to the acquisition of cognitive skills of adult humans, symbolic computation is stronger related to the early stages of performance whereas parallel distributed processing is related to later, highly practiced, performance. In order to model skill acquisition, two interacting connectionist systems are developed. The first system is able to implement symbolic data structures: it reliably stores and retrieves distributed activity patterns. It also can be used to match in parallel one activity pattern to all other stored patterns. This leads to an efficient solution of the variable binding problem and to parallel rule matching. A disadvantage of this system is that it can only focus on a fixed amount of knowledge at each moment in time. The second system - consisting of recurrent back-propagation networks - can be trained to process and to produce sequences of elements. After sufficient training with examples it possesses «dl advantages of parallel distributed processing, e. g., the direct application of knowledge without interpreting mechanisms. In contrast to the first system, these networks can learn to hold sequentially presented information of varying length simultaneously active in a highly distributed (superimposed) manner. In earlier systems - like the model of past-tense learning by Rumelhart and McClelland - such forms of encodings had to be done "by hand" with much human effort. These networks are also compared with the tensor product representation used by Smolensky.
